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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 678 Documents
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Usability of SIPAHAJI: An Information and Communication System Innovation to Support Jakarta Smart City Priono, Mochamad; Sufandi, Unggul Utan; Wiradharma, Gunawan; Prasetyo, Mario Aditya; Anam, Khaerul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2272

Abstract

This research addresses the problem of insufficient information and communication systems for halal tourism in Jakarta, which affects both tourists and local stakeholders. The study aims to evaluate the Android-based SIPAHAJI application, designed to support the Smart City concept by providing tourists with efficient and effective information about halal tourism destinations in Jakarta. The app employs collaborative filtering and location-based filtering to offer personalized recommendations based on user preferences and location. The methodology includes black box testing to assess the app's functionality and User Acceptance Testing (UAT) to evaluate user satisfaction. Respondents, consisting of experts and public stakeholders with professional experience or education in the tourism industry, were selected through quota sampling. Questionnaires were used for data collection. The results indicate that the application functions effectively, demonstrating excellent usability and high user acceptance. Respondents reported satisfaction with the app’s easy-to-use interface, which enhances the overall travel experience. Additionally, the app has the potential to optimize the Smart City concept, helping tourists navigate halal attractions and potentially increasing tourist visits, which could positively impact the local economy in alignment with the Sustainable Development Goals (SDGs).
Revitalizing Nusantara Traditions through Interactive Cultural Experiences with Augmented Reality Technology Dawis, Aisyah Mutia; Setiyanto, Sigit; Sadida, Irfan; Bariq, Faiq Fadhil Dzulfiqar
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2277

Abstract

Advances in digital technology are opening up new opportunities for cultural preservation and promotion. Augmented Reality (AR), with its ability to blend the real and digital worlds, has the potential to bridge the gap between younger generations and the rich cultural heritage of the Nusantara. This research aims to develop an AR application called "Imersi Budaya Nusantara" Immersion into Nusantara Culture to revitalize Nusantara traditions through interactive and educational cultural experiences. The application is expected to increase interest and understanding of Nusantara culture among younger generations while serving as an example of utilizing technology for cultural preservation. The research utilizes the Model Development Life Cycle (MDLC), which includes the stages of concept, design, material collection, development, testing, and distribution. The application's content includes visualizations of traditional dances, houses, food, clothing, and music from various regions in Indonesia. The "Imersi Budaya Nusantara" application has been successfully implemented and tested. The results of functionality and usability testing indicate that the application performs well and is user-friendly. User experience evaluations, based on questionnaires, demonstrate high levels of engagement, enjoyment, and educational value. This research proves that AR technology is effective in increasing interest and understanding of Nusantara culture among the younger generation. The "Imersi Budaya Nusantara" application serves as a model for utilizing technology for cultural preservation, opening up opportunities for further development and related research.
Optimizing Procurement Efficiency by Implementing K-Means and Random Forest in Kopegtel Samarinda’s Warehouse System Nikolas R, Fernando; Islamiyah, ⠀Islamiyah; Kamila, Vina Zahrotun
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2288

Abstract

Procurement is a company’s activity to purchase goods or equipment needed in operations. In the management process, a procurement management system is often used to facilitate this management, such as at CV Indocitra Multi Artha, which uses the “Sistem Warehouse Kopegtel Samarinda.” The system provides significant assistance to the company, but large requests can be overwhelming to be handled by the manager and can cause an overload information problem. Research was conducted to deal with these problems by implementing a data mining algorithm as a procurement recommendation system. K-means and Random Forest algorithms were chosen as methods for the research. The algorithm is processed within two critical steps, first by K-Means to get cluster data and then by predicting it with Random Forest to get a recommendation for whether the object should be bought or not. Hyperparameter tuning was performed to optimize the model’s performance, yielding an F1-Score of 86.95%, representing the balance between precision and recall, and an ROC AUC value of 82.34%. These substantial metric outcomes indicate that the model can provide practical recommendations
Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning Hussein, Ahmed Abdulmunem
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2290

Abstract

This paper addresses the Hybrid Flow Shop Scheduling Problem (HFSSP) by integrating metaheuristic (MHs) and machine learning (ML) approaches. Specifically, we propose a hybrid algorithm by combining Ant Colony Optimization (ACO) and Iterated Local Search (ILS) to form ACOILS. To further enhance the performance of this hybrid approach, we employ Proximal Policy Optimization (PPO), which is used for dynamic tuning of key parameters within the hybrid algorithm. The introduction of PPO allows real-time adjustment of key parameters, such as pheromone evaporation rates and local search intensity, to balance exploration and exploitation more effectively. Comparative experiments against the non-learning version of ACOILS and Simulated Annealing (SA) show that the learning based LACOILS significantly reduces the percentage deviation from the lower bound while maintaining stable performance through dynamic tuning. In terms of numerical results, LACOILS consistently outperforms SA and ACOILS. For smaller instances (N=20), it achieves up to 56.52% improvement over ACOILS and 12.5% over SA. For larger instances (N=150), LACOILS shows up to 29.82% improvement over ACOILS and 9.09% over SA, demonstrating its superior solution quality and efficiency.
Clustering Model for OKU Timur Script Images Toriko, Liu; Purnamasari, Susan Dian; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Andri, Andri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract— The OKU Timur is a regency located in South Sumatra Province. In the OKU Timur region there are many historical heritage sites, one of which is the script. In general, script is a system of symbols for writing language. The OKU Timur script is a writing system that is usually used by the local community. This writing system is characterized by its unique characters and has high historical and aesthetic value for the local community. The OKU Timur script is used in daily communication, traditional ceremonies, historical documents, and various other cultural contexts. This research aims to develop a clustering model that is used to efficiently and accurately group Of OKU Timur script images based on certain characteristics. By using techniques in the field of clustering such as the K-Means algorithm this model is developed so that the clustering of OKU Timur script images is made automatically in order to save time and effort. The study employs the K-Means algorithm to divide the data into several clusters, grouping data with similar characteristics into one cluster and data with different characteristics into another. This research is also expected to contribute to preserving digital culture so that the development of OKU Timur characters can be passed on to future generations.Keywords— OKU Timur Script, Clustering, K-Means
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Continuance Usage of Collaboration Tools after Social Distancing and The Influential Factors Salsabila, Aulia Rido; Wilantika, Nori; Santoso, Ibnu; Choir, Achmad Syahrul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Working from home (WFH) during the COVID-19 pandemic has challenges in terms of communication and coordination among employees due to the distance. Therefore, collaboration tools were needed during the COVID-19 pandemic. As we recover from the pandemic, the government revoked the social distancing policy restricting people's activities. The revoke is assumed to influence the continued use of collaboration tools. This study aims to understand the continuance usage of collaboration tools after no more social distancing. This study also seeks to identify the factors influencing the ongoing use of collaboration tools by integrating the Technology Acceptance Model (TAM) and Expectation Confirmatory Model (ECM). The method of data analysis employed was the partial least squares structural equation model (PLS-SEM). The findings indicated that most of 437 respondents kept using collaboration tools after no more social distancing. However, there was a decrease in the frequency of use. Our study findings have also proved that Actual Continued Usage is influenced by Continuance Intention by 43%. Furthermore, a factor that influences continuance intention the most is the attitude toward using collaboration tools. The results of this study also support the integration of TAM and ECM to examine user intentions and behavior regarding the continuance use of a technology.
Comparison of the performance of the C.45 algorithm with Naive Bayes in analyzing book borrowing at the Pringsewu Muhammadiyah University Library Wilian, Dani; Sriyanto, Sriyanto
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research investigates the effectiveness of the Naïve Bayes and C4.5 algorithms in analyzing book borrowing patterns at the Pringsewu Muhammadiyah University Library. As libraries evolve into important educational resource centers, understanding user borrowing behavior becomes critical for effective collection management and service improvement. This research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) to guide the research stages, including business understanding, data understanding, preparation, modeling, evaluation, and implementation. A dataset consisting of 5,586 records and ten attributes related to book lending was used, with thorough data cleaning and preprocessing performed. The performance of both algorithms was evaluated using K-fold cross validation, resulting in a C4.5 accuracy of 96.26% compared to 91.44% for Naïve Bayes. These results demonstrate that C4.5 excels at capturing complex relationships in data, providing valuable insights into user preferences and improving library services. This research highlights the potential of data mining techniques to improve library management and suggests directions for future research, including exploration of advanced machine learning algorithms and expansion of data sets for broader libraries.
Analysis of the Effect of Using the SIPLah Application on User Satisfaction in Procuring Elementary School Books in Pangkalpinang City Hamidah, Hamidah; Rizan, Okkita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2305

Abstract

The advancement of information technology has become more widespread across various sectors, particularly in education. The integration of information technology has increasingly simplified many aspects of human life. This study aims to evaluate the impact of the SIPLah (School Procurement Information System) application on user satisfaction in the procurement of books for elementary schools. SIPLah is a government-provided digital platform designed to streamline the procurement of goods and services within schools, promoting greater transparency, efficiency, and accountability. The research data was collected through surveys involving SIPLah users, such as school principals, teachers, and administrative staff engaged in book procurement. The study measured variables including ease of use, system reliability, procurement process speed, and user satisfaction with the procurement outcomes. The analysis revealed that the SIPLah application has a significant impact on user satisfaction. Among the factors studied, ease of use and system reliability had the most positive influence. However, some respondents reported technical challenges, such as delays in data updates and insufficient technical support. To enhance future user satisfaction, the study recommends improvements in technical services and system optimization.

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